Python, Data Science and Projects
description
About Python for Data Science
Python is the most popular programming language for Data Science as on Today. Python is powerful , easy to learn and flexible tool for coding Data Science and Machine Learning algorithms. In recent years, Python has evolved immensely with respect to Data Science sphere, with a huge community around Python creating quite a few power data science and analytics packages such as Pandas, Numpy, Scikit Learn, Scipy and more. As a result, analyzing data, modeling machine learning algorithms with Python has never been easier.
Python for Data Science - Course Objectives
- Python Fundamentals : Understanding Python syntax, data types, operators, conditional statements, functions. Writing simple Python scripts for Data Science
- Data Science essentials: Understand core concepts of Data Science. Data exploration, data mugging, data pre-processing and transforming Data for further analysis. Gain familiarity with Python packages Numpy and Pandas
- Python for Machine Learning(ML): Understand core concepts of Machine Learning and practice simple ML algorithms using Python package Scikit Learn
Course Contents
Introduction To Data Science With Python
- What is analytics & Data Science?
- Common Terms in Analytics
- Analytics vs. Data warehousing, OLAP, MIS Reporting
- Relevance in industry and need of the hour
- Types of problems and business objectives in various industries
- Critical success drivers
- Overview of analytics tools & their popularity
- Analytics Methodology & problem solving framework
Python: Essential (Core)
- Overview of Python- Starting with Python
- Introduction to installation of Python
- Introduction to Python Editors & IDE’s(Canopy, pycharm, Jupyter, Rodeo, Ipython etc…)
- Understand Jupyter notebook & Customize Settings
- Concept of Packages/Libraries – Important packages(NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc)
- Installing & loading Packages & Name Spaces
- Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries)
- List and Dictionary Comprehensions
Scientific Distribution Used In Python For Data Science
- Numpy, Spify, Pandas, Scikitlearn statmodels, Nltk etc
Accessing/Importing And Exporting Data Using Python Modules
- Importing Data from various sources (Csv, txt, excel, access etc)
- Database Input (Connecting to database)
- Viewing Data objects – subsetting, methods
- Exporting Data to various formats
- Important python modules: Pandas, beautifulsoup
Data Manipulation - Cleansing -Munging Using Python Modules
- Cleansing Data with Python
- Data Manipulation steps(Sorting, filtering, duplicates, merging, appending, subsetting, derived variables, sampling, Data type conversions, renaming, formatting etc)
- Data manipulation tools(Operators, Functions, Packages, control structures, Loops, arrays etc)
- Python Built-in Functions (Text, numeric, date, utility functions)
- Python User Defined Functions
- Stripping out extraneous information
- Normalizing data
- Formatting data
- Important Python modules for data manipulation (Pandas, Numpy, re, math, string, datetime etc)
Data Analysis - visualization Using Python
- Introduction exploratory data analysis
- Descriptive statistics, Frequency Tables and summarization
- Univariate Analysis (Distribution of data & Graphical Analysis)
- Bivariate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)
- Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ density etc)
- Important Packages for Exploratory Analysis(NumPy Arrays, Matplotlib, seaborn, Pandas and scipy.stats etc)
Introduction To Statistics
- Basic Statistics – Measures of Central Tendencies and Variance
- Building blocks – Probability Distributions – Normal distribution – Central Limit Theorem
- Inferential Statistics -Sampling – Concept of Hypothesis Testing
- Statistical Methods – Z/t-tests( One sample, independent, paired), Anova, Correlations and Chi-square
- Important modules for statistical methods: Numpy, Scipy, Pandas
Introduction To Predictive Modeling
- Concept of model in analytics and how it is used?
- Common terminology used in analytics & modeling process
- Popular modeling algorithms
- Types of Business problems – Mapping of Techniques
- Different Phases of Predictive Modeling
Lecturer
Our trainers are eager to share their knowledge and experience they have gained while working on critical projects in MNCs.
